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I am a research fellow with at the Flatiron Institute in New York, with joint appointment at the Center for Computational Astrophysics (CCA) and the Center for Computational Mathematics (CCM). Before this, I completed my Ph.D in Physics at the University of California, Berkeley. Prior to that, I did my undergraduate in Engineering Physics with a minor in Statistics from IIT, Bombay in India.

As a researcher, I am broadly interested in astrophysics (cosmology), machine learning and statistical inference. I spend most of my time thinking about computational approaches for cosmological analysis which will allow us to extract the maximum possible information about our Universe from large-scale galaxy surveys. Specifically, I develop forward modeling approaches that combine end-to-end modeling of cosmological observations using numerical simulations, starting all the way from the beginning of the Universe, with the state of the art Bayesian inference algorithms and machine learning tools for constraining cosmological models.

Thinking about high-dimensional inference in the context of cosmology has also led me to moonlight in the land of Bayesian statsitics. Here I am interested in developing asymptotically exact Markov Chain Monte Carlo methods and approximate variational methods for statistical inference in a more general setting of data-driven sciences. Occasionally, I foray into other scientific domains, such as my brief stints in COVID-19 data analaysis and exploring synthetic control methods in econometrics.

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